Extraction, processing, production and display of geographic data
Misagh Sepehry amin; Hassan Emami
Abstract
Extended AbstractIntroductionA digital orthophoto is a reliable, accurate, and low-cost map for acquiring knowledge, including geolocation, distance, area, and changes in imagery features. It is now considered one of the most widely used and sophisticated digital photogrammetry products. Orthophoto map ...
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Extended AbstractIntroductionA digital orthophoto is a reliable, accurate, and low-cost map for acquiring knowledge, including geolocation, distance, area, and changes in imagery features. It is now considered one of the most widely used and sophisticated digital photogrammetry products. Orthophoto map creation is substantially faster than traditional topographic map production because of the development of powerful algorithms for processing aerial, drone, ground, and satellite imagery. To begin, orthophoto is a result of photogrammetry processing that employs the Digital Terrain Model (DTM), which is commonly observed in classic aerial photogrammetry. In orthophotos, you will frequently notice an effect in which the terrain representation is very accurate, but there is a tilt in the buildings and other tall structures, which is caused by the use of DTM, which only maps the natural shape of the earth, excluding vegetation and all man-made objects and structures. A true orthophoto map provides a vertical view of the earth's surface, eliminating building tilting and providing access to practically any location on the ground. Traditionally, measuring digital surface models has been highly complex and costly. It is generally accomplished through the use of LiDAR or ground measurements. The end result of drone photogrammetry is known as an orthomosaic. In actuality, an orthomosaic is comparable to a true orthophoto (since it is formed using a digital surface model), but it is often not based on a metric camera with accurate focal length and internal dimensions, as they are expensive and not readily accessible for UAVs. Furthermore, orthomosaics may be generated using both nadir and oblique images. Drone-based orthomosaics are created based on the digital surface model rather than as a separate survey like traditional aerial photogrammetry. The DSM is produced by drone photogrammetry based on the 3D point cloud, which is the initial output of data processing. Materials & MethodsThe huge success of online services like Google Earth, Google Maps, Bing Maps, and so on increased demand for orthophotos, resulting in the development of new algorithms and sensors. It is commonly understood that orthophoto quality is determined by image resolution, camera calibration, orientation accuracy, and DTM accuracy. Because digital cameras produce high-resolution imagery, one of the most important consequences in orthophoto generation is the spatial resolution of the DTM: standing objects, such as buildings, plants, and so on, exhibit radial displacement in the final orthophoto. In practical applications, orthophotos are utilized as small and medium scale maps; updated earth surface maps; three-dimensional urban scene reconstruction; village surveying; land planning; precision agriculture; desertification monitoring; land use surveying; and other sectors. True orthophotos are orthophotos that have been improved to minimize tilt inaccuracy and projection discrepancies. The true orthophoto is exceedingly stringent with the original image; the heading overlap and side overlap are at least 80% and 60% overlap, respectively. Due to the reduction of displacements produced by camera tilt and height difference, the use of orthophoto as a spatial data format with high geometric accuracy has found growing applications in recent years. With the growing relevance of geographic information systems, particularly in metropolitan areas, the use of orthophoto in conjunction with spatial data has grown. Because orthophoto contains correct spatial and textural information about complications, it is feasible to produce virtual reality by integrating it with 3D models, where it is able to properly quantify the height and plane location of complications during 3D viewing. In this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images.Results and discussionThe result demonstrated the total error of orthomosaic generation from Google Earth imagery and UAV data to be 0.124 and 0.059 m/pixel, respectively. Moreover, the visual findings reveal that the edges of low-height barriers in the orthophoto generated from Google Earth images are superior to those in the orthophoto generated from drone imagery, but the edges of high-height obstacles, particularly those with noticeable shadows, are of poor quality. The findings of statistical parameters in quantitative surveys using randomly selected points in non-building regions revealed that the errors in the orthophoto derived from Google Earth data are 1.10 meters and 1.34 meters in terms of mean error and root mean square error (RMSE), respectively. In addition, the orthophoto generated from UAV data and Google Earth showed a 95% correlation and a 91% determination coefficient. In contrast, in building regions, the average height error and average square root error in the orthophoto generated from Google Earth data compared to UAV data were around 9 meters and 5 meters, respectively. Statistical metrics in these locations also revealed a low correlation of 80% and a determination coefficient of 65%.ConclusionsIn this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images. As a result, as the height of the obstacles and the presence of lengthy shadows increase, so does the inaccuracy of the height component of the orthophoto derived from Google Earth imagery. Therefore, it is advised that orthophotos for special applications, flat regions, and hills be created using Google Earth images. Additionally, Google Earth data offers the following advantages: free of charge; the utilization of historical imagery to generate orthophotos; and nearly four times less processing time and volume.
Mojdeh Ebrahimikia; Ali HosseiniNaveh
Abstract
Extended Abstract
Introduction
Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric ...
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Extended Abstract
Introduction
Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric information. The point clouds and the digital surface model used to build them are the two most important aspects that affect the quality of these images. On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Additionally, because of the presence of occluded regions and radiometric changes between overlapping images, approaches for creating point clouds based on image matching are unable to produce complete point clouds and contain flaws, particularly towards the outer edges of objects with high height differences. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds is proposed and explained in detail.
Materials & Methods
In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the purpose is to complete the incomplete points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were used to complete them, and then added to the existing point clouds in their actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos.
Results & Discussion
The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. These point clouds have been modified in the proposed method, and quantitative and qualitative comparisons demonstrate improved results in eliminating distortion in the majority of the buildings studied. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and precise form of the roof of each building and considering the outermost edges of the buildings.
Conclusion
The biggest amount of distortion on the edge of the roofs and their surroundings is caused by the lack of points on the structural edge of each roof, which is the boundary between dramatic height variations between the levels. By integrating these points with algorithms for recognizing and repairing edge lines, the resulting digital elevation model will be improved. This study presented a new method for completing the point cloud that enhanced the quality of true orthophoto edges, which was tested on a large number of building images and compared to the results of existing methods. In addition to implementing a new method for improving point clouds for orthophoto creation, the degree of distortion on the selected edge of four buildings has been greatly reduced when compared to the previous method. The success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy.
The proposed method did not uniformly reduce the distortions at the structural edges, and future advanced models could possibly improve it.
